Van der Waals quantum dots on layered hexagonal boron nitride
Bibliographic record
Abstract
Semiconductor quantum dots (QD) promise unique electronic, optical, and chemical properties, which can be exquisitely tuned by controlling the composition, size, and morphology. Semiconductor QDs have been synthesized primarily via two approaches, namely, epitaxial growth and wet-chemical synthesis. However, the properties of epitaxial QDs (eQDs) are susceptible to wetting layer formation and substrate dislocations, while colloidal QDs (cQDs) face fluorescence intermittency issues. Here, we report on the synthesis of a class of QDs that can overcome the fundamental limitations of eQDs and cQDs. By exploiting the sp 2 bonding of layered hexagonal boron nitride (hBN), we show that GaN QDs can be epitaxially grown through a weak van der Waals (vdW) interaction without two-dimensional wetting layer formation. The photoluminescence intensity of GaN van der Waals quantum dots (vQDs) is more than six times stronger than that of conventional GaN eQDs and no optical blinking was observed from vQDs. We show that the interadatom bond strength is about one order of magnitude stronger compared with that between the adatoms and the hBN substrate. This work shows that vQDs have unique properties that are difficult to achieve using existing QDs synthesis methods and thus can potentially enable new classes of high-performance optoelectronic and quantum devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".